Statistical Estimation of Perspective Damage Index for Vibration-Based Structural Health Monitoring

In the present work a piezoelectric based structural health monitoring (SHM) system is analyzed with the aim of assessing the ability of the piezoelectric patch to detect both edge and embedded delaminations proper of flange-skin composite laminated structures. The boundary element model is developed for piezoelectric solids and is implemented by taking advantage of the multidomain technique to model laminated and cracked configurations. A non-linear spring model interface is then implemented in conjunction with an iterative procedure allowing for the simulation of the finite stiffness of the bonding layers as well as of the non-penetration condition of the delamination surfaces. The dynamic behavior of the damaged structures and of the bonded piezoelectric patch is modeled by means of the dual reciprocity approach. To fully characterize the structure response the fracture mechanics behavior is studied in terms of energy release rate G and mode-mix phase angle Y. Finally, a damage index based on the electrical current output of the SHM system is introduced as an effective identification parameter of the flange-skin delamination occurrence.

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Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures based on Crack Image Analysis

Sensors ◽

10.3390/s19194304 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4304 ◽

Cited By ~ 2

Author(s):

Yang ◽

Chang ◽

Wu

Keyword(s):

Image Analysis ◽

Reinforced Concrete ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Structural Damage ◽

Measurement Method ◽

Damage Index ◽

Structural Health ◽

Damage Indices ◽

Indexing Method

Image analysis techniques have been applied to measure the displacements, strain field, and crack distribution of structures in the laboratory environment, and present strong potential for use in structural health monitoring applications. Compared with accelerometers, image analysis is good at monitoring area-based responses, such as crack patterns at critical regions of reinforced concrete (RC) structures. While the quantitative relationship between cracks and structural damage depends on many factors, cracks need to be detected and quantified in an automatic manner for further investigation into structural health monitoring. This work proposes a damage-indexing method by integrating an image-based crack measurement method and a crack quantification method. The image-based crack measurement method identifies cracks locations, opening widths, and orientations. Fractal dimension analysis gives the flexural cracks and shear cracks an overall damage index ranging between 0 and 1. According to the orientations of the cracks analyzed by image analysis, the cracks can be classified as either shear or flexural, and the overall damage index can be separated into shear and flexural damage indices. These damage indices not only quantify the damage of an RC structure, but also the contents of shear and flexural failures. While the engineering significance of the damage indices is structure dependent, when the damage indexing method is used for structural health monitoring, the damage indices safety thresholds can further be defined based on the structure type under consideration. Finally, this paper demonstrates this method by using the results of two experiments on RC tubular containment vessel structures.

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Composite Multiscale Cross-Sample Entropy Analysis for Long-Term Structural Health Monitoring of Residential Buildings

Entropy ◽

10.3390/e23010060 ◽

2020 ◽

Vol 23 (1) ◽

pp. 60

Author(s):

Tzu-Kang Lin ◽

Dong-You Lee

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Residential Buildings ◽

Damage Index ◽

Sample Entropy ◽

Ambient Vibration ◽

Coarse Grained ◽

Monitoring Site ◽

Structural Health

This study proposesd a novel, entropy-based structural health monitoring (SHM) system for measuring microvibration signals generated by actual buildings. A structural health diagnosis interface was established for demonstration purposes. To enhance the reliability and accuracy of entropy evaluation at various scales, composite multiscale cross-sample entropy (CMSCE) was adopted to increase the number of coarse-grained time series. The degree of similarity and asynchrony between ambient vibration signals measured on adjacent floors was used as an in-dicator for structural health assessment. A residential building that has been monitored since 1994 was selected for long-term monitoring. The accumulated database, including both the earthquake and ambient vibrations in each seismic event, provided the possibility to evaluate the practicability of the CMSCE-based method. Entropy curves obtained for each of the years, as well as the stable trend of the corresponding damage index (DI) graphs, demonstrated the relia-bility of the proposed SHM system. Moreover, two large earthquake events that occurred near the monitoring site were analyzed. The results revealed that the entropy values may have been slightly increased after the earthquakes. Positive DI values were obtained for higher floors, which could provide an early warning of structural instability. The proposed SHM system is highly stable and practical.

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Compressive sensing based data-loss recovery enables robust estimation of damage index in ultrasonic structural health monitoring

2020 IEEE Sensors ◽

10.1109/sensors47125.2020.9278599 ◽

2020 ◽

Author(s):

Shruti Sawant ◽

Sauvik Banerjee ◽

Siddharth Tallur

Keyword(s):

Structural Health Monitoring ◽

Compressive Sensing ◽

Robust Estimation ◽

Health Monitoring ◽

Damage Index ◽

Data Loss ◽

Loss Recovery ◽

Structural Health

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Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy

Entropy ◽

10.3390/e21010041 ◽

2019 ◽

Vol 21 (1) ◽

pp. 41 ◽

Cited By ~ 6

Author(s):

Tzu-Kang Lin ◽

Yi-Hsiu Chien

Keyword(s):

Structural Health Monitoring ◽

Monitoring System ◽

Health Monitoring ◽

Confusion Matrix ◽

Damage Index ◽

Sample Entropy ◽

Experimental Models ◽

Health Monitoring System ◽

Structural Health ◽

Structural Health Monitoring System

The aim of this study was to develop an entropy-based structural health monitoring system for solving the problem of unstable entropy values observed when multiscale cross-sample entropy (MSCE) is employed to assess damage in real structures. Composite MSCE was utilized to enhance the reliability of entropy values on every scale. Additionally, the first mode of a structure was extracted using ensemble empirical mode decomposition to conduct entropy analysis and evaluate the accuracy of damage assessment. A seven-story model was created to validate the efficiency of the proposed method and the damage index. Subsequently, an experiment was conducted on a seven-story steel benchmark structure including 15 damaged cases to compare the numerical and experimental models. A confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. The results revealed the feasibility of the modified structural health monitoring system and demonstrated its potential in the field of long-term monitoring.

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